- Expose stats.seriesFetched at `/api/v1/query_range` responses too
for the sake of consistency.
- Initialize QueryStats when it is needed and pass it to EvalConfig then.
This guarantees that the QueryStats is properly collected when the query
contains some subqueries.
The change adds a new field `seriesFetched` to EvalConfig object.
Since EvalConfig object can be copied inside `Exec`,
`seriesFetched` is a pointer which can be updated by all copied
objects.
The reason for having stats is that other components, like vmalert,
could benefit from this information.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
- Allocate and initialize seriesByWorkerID slice in a single go instead
of initializing every item in the list separately.
This should reduce CPU usage a bit.
- Properly set anti-false sharing padding at timeseriesWithPadding structure
- Document the change at docs/CHANGELOG.md
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3966
* vmselect/promql: refactor `evalRollupNoIncrementalAggregate` to use lock-less approach for parallel workers computation
Locking there is causing issues when running on highly multi-core system as it introduces lock contention during results merge.
New implementation uses lock less approach to store results per workerID and merges final result in the end, this is expected to significantly reduce lock contention and CPU usage for systems with high number of cores.
Related: #3966
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
* vmselect/promql: add pooling for `timeseriesWithPadding` to reduce allocations
Related: #3966
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
* vmselect/promql: refactor `evalRollupFuncWithSubquery` to avoid using locks
Uses same approach as `evalRollupNoIncrementalAggregate` to remove locking between workers and reduce lock contention.
Related: #3966
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
---------
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
This opens the possibility to remove tssLock from evalRollupFuncWithSubquery()
in the follow-up commit from @zekker6 in order to speed up the code
for systems with many CPU cores.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3966
Note that the parallel execution of `union()` args may take more memory and CPU time
than the sequential execution if args contain heavy queries, which may load all the available CPU,
disk and memory resources and vmselect and vmstorage levels.
- Use getScalar() function for obtaining the expected scalar from phi arg
- Reduce the error message returned to the user when incorrect phi is passed to histogram_quantiles
- Improve the description of this bugfix in the docs/CHANGELOG.md
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3026
* Explicitly store a pointer to UserReadableError in the error interface.
Previously Go automatically converted the value to a pointer before storing in the error interface.
* Add Unwrap() method to UserReadableError, so it can be used transparently with the other code,
which calls errors.Is() and errors.As().
* Document the change in docs/CHANGELOG.md
When read query fails, VM returns rich error message with
all the details. While these details might be useful
for debugging specific cases, they're usually too verbose
for users.
Introducing a new error type `UserReadableError` is supposed
to allow to return to user only the most important parts
of the error trace. This supposed to improve error readability
in web interfaces such as VMUI or Grafana.
The full error trace is still logged with the full context
and can be found in vmselect logs.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Signed-off-by: hagen1778 <roman@victoriametrics.com>
- show dates in human-readable format, e.g. 2022-05-07, instead of a numeric value
- limit the maximum length of queries and filters shown in trace messages
This option allows reducing CPU usage a bit when VictoriaMetrics is used
for collecting and processing non-Prometheus data. For example, InfluxDB line protocol, Graphite, OpenTSDB, CSV, etc.
